text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  
  import argparse
  import collections
  import json
  import os
  import random
  import sys
  import time
  import uuid
  
  import numpy as np
  import PIL
  import torch
  import torchvision
  import torch.utils.data
  
  from domainbed import datasets
  from domainbed import hparams_registry
  from domainbed import algorithms
  from domainbed.lib import misc
  from domainbed.lib.fast_data_loader import InfiniteDataLoader, FastDataLoader
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description='Domain generalization')
      parser.add_argument('--data_dir', type=str)
      parser.add_argument('--dataset', type=str, default="RotatedMNIST")
      parser.add_argument('--algorithm', type=str, default="ERM")
      parser.add_argument('--task', type=str, default="domain_generalization",
          choices=["domain_generalization", "domain_adaptation"])
      parser.add_argument('--hparams', type=str,
          help='JSON-serialized hparams dict')
      parser.add_argument('--hparams_seed', type=int, default=0,
          help='Seed for random hparams (0 means "default hparams")')
      parser.add_argument('--trial_seed', type=int, default=0,
          help='Trial number (used for seeding split_dataset and '
          'random_hparams).')
      parser.add_argument('--seed', type=int, default=0,
          help='Seed for everything else')
      parser.add_argument('--steps', type=int, default=None,
          help='Number of steps. Default is dataset-dependent.')
      parser.add_argument('--checkpoint_freq', type=int, default=None,
          help='Checkpoint every N steps. Default is dataset-dependent.')
      parser.add_argument('--test_envs', type=int, nargs='+', default=[0])
      parser.add_argument('--output_dir', type=str, default="train_output")
      parser.add_argument('--holdout_fraction', type=float, default=0.2)
      parser.add_argument('--uda_holdout_fraction', type=float, default=0,
          help="For domain adaptation, % of test to use unlabeled for training.")
      parser.add_argument('--skip_model_save', action='store_true')
      parser.add_argument('--save_model_every_checkpoint', action='store_true')
      args = parser.parse_args()
  
      # If we ever want to implement checkpointing, just persist these values
      # every once in a while, and then load them from disk here.
      start_step = 0
      algorithm_dict = None
  
      os.makedirs(args.output_dir, exist_ok=True)
      sys.stdout = misc.Tee(os.path.join(args.output_dir, 'out.txt'))
      sys.stderr = misc.Tee(os.path.join(args.output_dir, 'err.txt'))
  
      print("Environment:")
      print("\tPython: {}".format(sys.version.split(" ")[0]))
      print("\tPyTorch: {}".format(torch.__version__))
      print("\tTorchvision: {}".format(torchvision.__version__))
      print("\tCUDA: {}".format(torch.version.cuda))
      print("\tCUDNN: {}".format(torch.backends.cudnn.version()))
      print("\tNumPy: {}".format(np.__version__))
      print("\tPIL: {}".format(PIL.__version__))
  
      print('Args:')
      for k, v in sorted(vars(args).items()):
          print('\t{}: {}'.format(k, v))
  
      if args.hparams_seed == 0:
          hparams = hparams_registry.default_hparams(args.algorithm, args.dataset)
      else:
          hparams = hparams_registry.random_hparams(args.algorithm, args.dataset,
              misc.seed_hash(args.hparams_seed, args.trial_seed))
      if args.hparams:
          hparams.update(json.loads(args.hparams))
  
      print('HParams:')
      for k, v in sorted(hparams.items()):
          print('\t{}: {}'.format(k, v))
  
      random.seed(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      torch.backends.cudnn.deterministic = True
      torch.backends.cudnn.benchmark = False
  
      if torch.cuda.is_available():
          device = "cuda"
      else:
          device = "cpu"
  
      if args.dataset in vars(datasets):
          dataset = vars(datasets)[args.dataset](args.data_dir,
              args.test_envs, hparams)
      else:
          raise NotImplementedError
  
      # Split each env into an 'in-split' and an 'out-split'. We'll train on
      # each in-split except the test envs, and evaluate on all splits.
  
      # To allow unsupervised domain adaptation experiments, we split each test
      # env into 'in-split', 'uda-split' and 'out-split'. The 'in-split' is used
      # by collect_results.py to compute classification accuracies.  The
      # 'out-split' is used by the Oracle model selectino method. The unlabeled
      # samples in 'uda-split' are passed to the algorithm at training time if
      # args.task == "domain_adaptation". If we are interested in comparing
      # domain generalization and domain adaptation results, then domain
      # generalization algorithms should create the same 'uda-splits', which will
      # be discared at training.
      in_splits = []
      out_splits = []
      uda_splits = []
      for env_i, env in enumerate(dataset):
          uda = []
  
          out, in_ = misc.split_dataset(env,
              int(len(env)*args.holdout_fraction),
              misc.seed_hash(args.trial_seed, env_i))
  
          if env_i in args.test_envs:
              uda, in_ = misc.split_dataset(in_,
                  int(len(in_)*args.uda_holdout_fraction),
                  misc.seed_hash(args.trial_seed, env_i))
  
          if hparams['class_balanced']:
              in_weights = misc.make_weights_for_balanced_classes(in_)
              out_weights = misc.make_weights_for_balanced_classes(out)
              if uda is not None:
                  uda_weights = misc.make_weights_for_balanced_classes(uda)
          else:
              in_weights, out_weights, uda_weights = None, None, None
          in_splits.append((in_, in_weights))
          out_splits.append((out, out_weights))
          if len(uda):
              uda_splits.append((uda, uda_weights))
  
      if args.task == "domain_adaptation" and len(uda_splits) == 0:
          raise ValueError("Not enough unlabeled samples for domain adaptation.")
  
      train_loaders = [InfiniteDataLoader(
          dataset=env,
          weights=env_weights,
          batch_size=hparams['batch_size'],
          num_workers=dataset.N_WORKERS)
          for i, (env, env_weights) in enumerate(in_splits)
          if i not in args.test_envs]
  
      uda_loaders = [InfiniteDataLoader(
          dataset=env,
          weights=env_weights,
          batch_size=hparams['batch_size'],
          num_workers=dataset.N_WORKERS)
          for i, (env, env_weights) in enumerate(uda_splits)
          if i in args.test_envs]
  
      eval_loaders = [FastDataLoader(
          dataset=env,
          batch_size=64,
          num_workers=dataset.N_WORKERS)
          for env, _ in (in_splits + out_splits + uda_splits)]
      eval_weights = [None for _, weights in (in_splits + out_splits + uda_splits)]
      eval_loader_names = ['env{}_in'.format(i)
          for i in range(len(in_splits))]
      eval_loader_names += ['env{}_out'.format(i)
          for i in range(len(out_splits))]
      eval_loader_names += ['env{}_uda'.format(i)
          for i in range(len(uda_splits))]
  
      algorithm_class = algorithms.get_algorithm_class(args.algorithm)
      algorithm = algorithm_class(dataset.input_shape, dataset.num_classes,
          len(dataset) - len(args.test_envs), hparams)
  
      if algorithm_dict is not None:
          algorithm.load_state_dict(algorithm_dict)
  
      algorithm.to(device)
  
      train_minibatches_iterator = zip(*train_loaders)
      uda_minibatches_iterator = zip(*uda_loaders)
      checkpoint_vals = collections.defaultdict(lambda: [])
  
      steps_per_epoch = min([len(env)/hparams['batch_size'] for env,_ in in_splits])
  
      n_steps = args.steps or dataset.N_STEPS
      checkpoint_freq = args.checkpoint_freq or dataset.CHECKPOINT_FREQ
  
      def save_checkpoint(filename):
          if args.skip_model_save:
              return
          save_dict = {
              "args": vars(args),
              "model_input_shape": dataset.input_shape,
              "model_num_classes": dataset.num_classes,
              "model_num_domains": len(dataset) - len(args.test_envs),
              "model_hparams": hparams,
              "model_dict": algorithm.state_dict()
          }
          torch.save(save_dict, os.path.join(args.output_dir, filename))
  
  
      last_results_keys = None
      for step in range(start_step, n_steps):
          step_start_time = time.time()
          minibatches_device = [(x.to(device), y.to(device))
              for x,y in next(train_minibatches_iterator)]
          if args.task == "domain_adaptation":
              uda_device = [x.to(device)
                  for x,_ in next(uda_minibatches_iterator)]
          else:
              uda_device = None
          step_vals = algorithm.update(minibatches_device, uda_device)
          checkpoint_vals['step_time'].append(time.time() - step_start_time)
  
          for key, val in step_vals.items():
              checkpoint_vals[key].append(val)
  
          if (step % checkpoint_freq == 0) or (step == n_steps - 1):
              results = {
                  'step': step,
                  'epoch': step / steps_per_epoch,
              }
  
              for key, val in checkpoint_vals.items():
                  results[key] = np.mean(val)
  
              evals = zip(eval_loader_names, eval_loaders, eval_weights)
              for name, loader, weights in evals:
                  acc = misc.accuracy(algorithm, loader, weights, device)
                  results[name+'_acc'] = acc
  
              # results['mem_gb'] = torch.cuda.max_memory_allocated() / (1024.*1024.*1024.)
              results['mem_gb'] = 20
  
              results_keys = sorted(results.keys())
              if results_keys != last_results_keys:
                  misc.print_row(results_keys, colwidth=12)
                  last_results_keys = results_keys
              misc.print_row([results[key] for key in results_keys],
                  colwidth=12)
  
              results.update({
                  'hparams': hparams,
                  'args': vars(args)
              })
  
              epochs_path = os.path.join(args.output_dir, 'results.jsonl')
              with open(epochs_path, 'a') as f:
                  f.write(json.dumps(results, sort_keys=True) + "\n")
  
              algorithm_dict = algorithm.state_dict()
              start_step = step + 1
              checkpoint_vals = collections.defaultdict(lambda: [])
  
              if args.save_model_every_checkpoint:
                  save_checkpoint(f'model_step{step}.pkl')
  
      save_checkpoint('model.pkl')
  
      with open(os.path.join(args.output_dir, 'done'), 'w') as f:
          f.write('done')
  